A recommendation service is a computer-implemented service that recommends items from a database of items. The recommendations are customized to particular users based on information known about the users. One common application for recommendation services involves recommending products to online customers. For example, online merchants commonly provide services for recommending products (books, compact discs, videos, etc.) to customers based on profiles that have been developed for such customers. Recommendation services are also common for recommending Web sites, articles, and other types of informational content to users.
One technique commonly used by recommendation services is known as content-based filtering. Pure content-based systems operate by attempting to identify items which, based on an analysis of item content, are similar to items that are known to be of interest to the user. For example, a content-based Web site recommendation service may operate by parsing the user's favorite Web pages to generate a profile of commonly-occurring terms, and then use this profile to search for other Web pages that include some or all of these terms.
Content-based systems have several significant limitations. For example, content-based methods generally do not provide any mechanism for evaluating the quality or popularity of an item. In addition, content-based methods generally require that the items include some form of content that is amenable to feature extraction algorithms; as a result, content-based systems tend to be poorly suited for recommending products and other types of items that have little or no useful, parsable content.
Another common recommendation technique is known as collaborative filtering. In a pure collaborative system, items are recommended to users based on the interests of a community of users, without any analysis of item content. Collaborative systems commonly operate by having the users explicitly rate individual items from a list of popular items. Some systems, such as those described in instead require users to create lists of their favorite items. See U.S. Pat. Nos. 5,583,763 and 5,749,081. Through this explicit rating or list creating process, each user builds a personal profile of his or her preferences. To generate recommendations for a particular user, the user's profile is compared to the profiles of other users to identify one or more “similar users.” Items that were rated highly by these similar users, but which have not yet been rated by the user, are then recommended to the user. An important benefit of collaborative filtering is that it overcomes the above-noted deficiencies of content-based filtering.
As with content-based filtering methods, however, existing collaborative filtering techniques have several problems. One problem is that users of online stores frequently do not take the time to explicitly rate the products, or create lists of their favorite products. As a result, the online merchant may be able to provide personalized product recommendations to only a small segment of its customers.
Further, even if a user takes the time to set up a profile, the recommendations thereafter provided to the user typically will not take into account the user's short term shopping or browsing interests. For example, the recommendations may not be helpful to a user who is purchasing a gift for another user, or who is venturing into an unfamiliar product category.
Another problem with collaborative filtering techniques is that an item in the database normally cannot be recommended until the item has been rated. As a result, the operator of a new collaborative recommendation system is commonly faced with a “cold start” problem in which the service cannot be brought online in a useful form until a threshold quantity of ratings data has been collected. In addition, even after the service has been brought online, it may take months or years before a significant quantity of the database items can be recommended. Further, as new items are added to the catalog (such as descriptions of newly released products), these new items may not recommendable by the system for a period of time.
Another problem with collaborative filtering methods is that the task of comparing user profiles tends to be time consuming, particularly if the number of users is large (e.g., tens or hundreds of thousands). As a result, a tradeoff tends to exist between response time and breadth of analysis. For example, in a recommendation system that generates real-time recommendations in response to requests from users, it may not be feasible to compare the user's ratings profile to those of all other users. A relatively shallow analysis of the available data (leading to poor recommendations) may therefore be performed.
Another problem with both collaborative and content-based systems is that they generally do not reflect the current preferences of the community of users. In the context of a system that recommends products to customers, for example, there is typically no mechanism for favoring items that are currently “hot sellers.” In addition, existing systems typically do not provide a mechanism for recognizing that the user may be searching for a particular type or category of item.